With respect to knowledge-dependent approaches (KDAs) that require optimization in the high-dimensional parameter space, data-driven methods (DDMs) show remarkable generalization and diversity but commonly with unsatisfactory accuracy for complex systems. To overcome the imperfections of the KDAs and DDMs, we suggest a composite scheme by combining them, which not only alleviates the optimization burden but also presents a remarkable generalization and accuracy. This composite scheme as an example is applied to design one-dimensional photonic crystals (1DPCs) from the transmission spectra, which first determines the 1DPC type by a classification neural network, then predicts the layer thicknesses of that 1DPC by a generative adversarial network (GAN), and finally further optimizes the layer thicknesses by the KDA that is based on the method of least squares and starts from the results of the KDA. Numerical results yield that the third step can improve more than 12% for the predi...

中文翻译：

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结合机器学习和知识依赖型优化的高精度逆光学设计

对于需要在高维参数空间中进行优化的知识相关方法（KDA），数据驱动方法（DDM）具有显着的泛化性和多样性，但对于复杂系统而言通常精度不令人满意。为了克服KDA和DDM的不完善之处，我们提出了一种将它们组合起来的组合方案，该方案不仅减轻了优化负担，而且具有显着的概括性和准确性。以该复合方案为例，根据透射光谱设计一维光子晶体（1DPC），首先通过分类神经网络确定1DPC类型，然后通过生成对抗网络（GAN）预测该1DPC的层厚。 ），最后，通过最小二乘法从KDA的结果开始，通过KDA进一步优化层厚度。数值结果表明，第三步可以将预制件提高12％以上。